Despite growing use of 3D city models (3DCMs) and urban digital twins (UDTs), web tools for their processing and visualization remain scarce. We present an interoperable, high-performance web application composed of a 3D tiler and a WebGPU viewer that enables scalable conversion, streaming, and rendering of urban datasets in compliance with open standards. The proposed system allows users to explore large-scale 3DCMs interactively without local installations. A showcase visualizing quality-validation results for a 3DCM demonstrates practical value. Experiments confirm that 3D Tiles 1.1 standard enables scalable data management and richer interaction, whereas WebGPU offers up to 7x better rendering performance on modern hardware. By presenting this solution and usage example, we aim to foster development of next-generation web-based 3D geospatial, digital-twin, and metaverse solutions.

This paper performs, describes, and evaluates a comparison of seven software tools (ArcGIS Pro, GRASS GIS, SAGA GIS, CitySim, Ladybug, SimStadt, and UMEP) to calculate solar irradiation. The analysis focuses on data requirements, software usability, and accuracy simulation output. The use case for the comparison is solar irradiation on building surfaces, in particular on roofs. The research involves collecting and preparing spatial and weather data. Two test areas—the Santana district in São Paulo, Brazil, and the Heino rural area in Raalte, the Netherlands—were selected. In both cases, the study area encompasses the vicinity of a weather station. Therefore, the meteorological data from these stations serve as ground truth for the validation of the simulation results. We create several models (raster and vector) to meet the diverse input requirements. We present our findings and discuss the output from the software tools from both quantitative and qualitative points of view. Vector-based simulation models offer better results than raster-based ones. However, they have more complex data requirements. Future research will focus on evaluating the quality of the simulation results on vertical and tilted surfaces as well as the calculation of direct and diffuse solar irradiation values for vector-based methods.

Building information modelling (BIM) and geoinformation are widely recognised as complementary sources of data. Whereas a BIM model can represent a single building or infrastructure project in high detail, geoinformation-based sources can represent different types of features in a large region with less detail. Integrating geoinformation and BIM is very useful in practice and constitutes an active research field—often referred to as GeoBIM. A short list of GeoBIM applications include: performing checks for the issuance of building permits using buildings (BIM) and city regulations (Geo), navigation that combines outdoor (Geo) and indoor (BIM) portions, facility management for infrastructure sites (BIM) that include the regional connections between the sites (Geo), and risk management using regional simulations (Geo) that also takes into account the impact on specific sites (BIM). [...]

In response to peer review feedback, the article underwent several key revisions to enhance clarity and academic rigour. First, the authors incorporated new and relevant literature, including a 2024 study on digital building permits and logbooks, as well as a 2023 paper on digital sustainability in horticulture. These additions aim to strengthen the theoretical foundation and contextual relevance of the study. To address concerns about theoretical depth, the Introduction was revised to provide a more precise explanation of the study’s contribution: a replicable method for mapping digital construction practices to global sustainability targets and identifying DBP and DBL practices that advance sustainable construction and building management. The Discussion and Conclusions sections were expanded to emphasise the unique contribution of the study, namely, the first systematic mapping of Digital Building Permit (DBP) and Digital Building Logbook (DBL) practices to specific UN Sustainable Development Goals (SDGs). Methodological transparency was improved by detailing the purposive sampling of experts, the rationale for the four-phase research design, and the tools used (Slido and Miro) for data collection and validation. Clarifications were added regarding the qualitative nature of the study, the absence of data normalisation, and the anonymisation of workshop responses. Language and formatting were also refined. Grammatical errors were corrected, long sentences shortened, and citation formatting reviewed. The figure and the table were verified for proper citation. Finally, the revised Conclusions section now explicitly acknowledges the study’s limitations, including its European focus, the use of single-point-in-time data collection, and the qualitative nature of its findings.

In the abstract, there is a typo in the name of the study area in the Netherlands: It is “Henio,” it should be “Heino.” [...]

Urban morphological analytics on buildings is informative for sustainable development. 3D building massing features, such as courtyards and setbacks, reflect spatial organizations and circulations, while influence daylight access, ventilation, and shading. However, existing 3D GIS methods usually overlook such 3D massing features, further obscure morphological analytics and environmental assessment. This article proposes MorphCut, an efficient convex decomposition method that segments 3D shapes into mass-aligned parts. MorphCut leverages key morphological properties—planarity, regularity, and Gestalt laws—after a topological preprocessing step to enable mass-aware decomposition. Experiments on representative samples, ranging from small houses to complex skyscrapers, showed that MorphCut outperformed four baseline methods in (i) balancing convexity and compactness, (ii) aligning decomposed parts with building masses, and (iii) preserving geometric fidelity (average deviation: 0.25 m). An urban-scale validation on datasets from Delft and Hong Kong, comprising over 30,000 buildings across 18.3 km², demonstrated MorphCut’s robustness, scalability, and generalizability. MorphCut successfully decomposed 98% of buildings in low-rise regions (+78% over the second-best method) and 93% in high-rise areas (+2%), completing processing in 13 hours (3 hours faster). These results position MorphCut as a foundational 3D GIS tool for large-scale, mass-aware morphological analysis, with implications for digital twins, sustainable planning, and environmental modeling.

This paper presents an implemented methodology to convert highly detailed building information models (BIMs) into geospatial 3D city models (Geos) at multiple levels of detail (LoDs). As BIM models contain highly detailed and complex geometries that differ significantly from city model standards, abstraction and conversion methods are required to generate usable outputs. Our study addresses this by developing a methodology that generates nine different LoDs from a single IFC input. These LoDs include both volumetric and surface-based abstractions for exterior and interior representations. The methodology involves voxelisation, filtering and simplification of surfaces, footprint derivation, storey abstraction, and interior geometry extraction. Together, these approaches allow flexible conversion tailored to specific applications, balancing accuracy, complexity, and computational efficiency. The methodology is implemented in a prototype tool named IfcEnvelopeExtractor. It automates IFC-to-CityGML/CityJSON conversion with minimal user input. The methodology was tested on a variety of models ranging from small houses to multistorey buildings. The evaluation covered geometric accuracy, semantic accuracy, and model complexity. Results show that non-volumetric abstractions and interior abstractions performed very well, producing robust and accurate results. However, the accuracy decreased for volumetric and complex abstractions, particularly at higher LoDs. Problems included missing or incorrectly trimmed surfaces, and modelling gaps and tolerance issues in the input IFC models. These limitations reveal that the quality of the input BIM models significantly affects the reliability of conversions. Overall, the methodology demonstrates that automated, flexible, and open-source solutions can effectively bridge the gap between BIM and geospatial domains, contributing to scalable GeoBIM integration in practice.

Data-driven prediction of infrastructure aging is challenging due to the complex stochastic nature of degradation effects and the ill-documented historical records. Degradation modeling is, however, crucial for predictive maintenance that is key for infrastructure integrity. This study presents a multi-attribute, data-driven approach for modelling stochastic degradation and maintenance effects of roads, mining an extensive database of geo-located historical inspection and maintenance records from the municipality of Amsterdam. Inspection data track pavement conditions at irregular intervals across ten discrete states per road segment, following the Dutch CROW 146 protocol. Damage severity and extent for eight damage modes is captured, i.e., for transverse unevenness, irregularities, ravelling, edge damage, crack formation, joint filling, joint width, and settling. The maintenance dataset includes >25k minor interventions across 17k road segments, indicating repair requirements, and 200+ major maintenance projects, covering 21k segments where interventions are planned, all without verifying completion. This complicates accurate modelling of natural degradation as it is confounded by maintenance effects. To address the issue of irregular inspections, degradation is first modelled as a continuous-time Markov chain. Thereby, transition rates are estimated, which are then converted to discrete-time Markov chain transition probability matrices to eventually support regular maintenance planning. We further learn the effects of minor and major maintenance activities, as defined and recorded in the database. Based on the estimated degradation transitions, pre-maintenance and post-maintenance state distributions are estimated. Instantaneous maintenance transition matrices are computed by minimizing the cross-entropy between the pre-maintenance state after the intervention and the post-maintenance state. The model allows for a multi-attribute approach, segmenting roads based on construction material (e.g., asphalt, tiled pavement) and traffic loads (e.g., residential, commercial/pedestrian). The approach is exemplified for tiled pavements for a section of the road network of Amsterdam, where the effects of minor and major maintenance are ablated for long-term predictions. Although applied to Amsterdam, this method is relevant to any infrastructure system with discrete state datasets, providing a foundation for data-driven decision-making in infrastructure management.

This paper suggests a method to simulate interactions between buildings and their outdoor conditions at the city-scale using a coupled scheme whose physical parameters are entirely assessed from data of the indoor and outdoor built environment. The coupled scheme consists of a reduced order building energy model and a single layer urban canopy model. In a previous study, it was proven that physical parameters of a single layer urban canopy model can be assessed using measurements of the outdoor temperature and humidity in a street canyon. For the coupled scheme to be fully data driven, the next step is to demonstrate that the reduced order building energy model can estimate the cooling consumption and exterior wall surface temperature in good agreement with measurements or simulated data after being trained using machine learning. Indeed, results show that a multi objective genetic algorithm can find values for physical parameters of the reduced order building energy model. Estimates of the cooling consumption and exterior wall surface temperature provided by the trained model achieve a CV-RMSE below 10% and a RMSE lower than 2.5 Kelvin, respectively, with respect to data generated from EnergyPlus. The last step towards a full data driven coupled scheme for city-scale simulations would be to iteratively train the reduce order building energy model with the single layer urban canopy model and show the convergence and accuracy of their respective outputs.

Historic gardens, regarded as a significant genre of cultural heritage, encapsulate the enduring essence of bygone eras while concurrently transcending temporal boundaries to resonate with the present and future. These gardens provide us vitality and inspiration, holding a collective repository of human memory and serving as a testament to our shared heritage. However, like landscapes, gardens constantly change through natural processes and human interventions. How can we preserve these gardens, though changes are unavoidable? Spatial and visual characteristics are the gardens' essential characteristics, and point-cloud (LiDAR) technologies are powerful tools to reveal and analyze gardens’ spatial-visual relationships and characteristics. Therefore, this paper aims to present a point-cloud-based approach to identifying spatial-visual design principles and making them operational to protect and develop historic gardens. Additionally, several methods have been proposed in this research, including (a) a voxel-based method to transfer points into a solid model for GIS-based computation, (b) a novel method to analyze the field of view (FOV), and (c) a systemic framework to reveal historic gardens’ spatial-visual characteristics based on the voxelized model. Jichang Garden, a historic garden in Wuxi, China, known for its visual design and spatial arrangement, has been selected as a case study to showcase how to apply the methods proposed by this paper. The findings include the design principles for the water body, the arrangement for a route, and the planting strategies of the garden. The conservational strategies have been formed based on the findings, and the appliable potentials and limitations of the methods have also been discussed.

With the growing demand for sustainable accountability, the European Directive 2014/24/EU (EU 2014) pushes architects to deliver Building Information Models (BIM) as a part of procurement processes for public buildings. In the Netherlands, BIM model data is relevant to the building permitting process, which involves an environmental performance calculation (MPG). This assessment takes into consideration the embodied carbon of materials in a building. Although this analysis is performed by a qualified expert in late design phases, architects benefit from integrating carbon data in early design decision-making. Design methods supported by Life Cycle Assessment (LCA) values are needed before involving expert collaborators, and not only when applying for a building permit. The existing carbon assessment tools require detailed data from BIM models, which are often not available at early design phases. Simplified tools have been discussed in theory, and explored in their potential applications, however, there lacks scientific literature discussing the hurdles designers face in their attempt to create such tools in practice, for their internal use throughout early design phases. This paper focuses on the architecture professional practice and design methods supported by digital and computational technologies, regarding embodied carbon data. It investigates the challenges in integrating embodied carbon data in the design workflow, through the development of a digital tool made by designers, for designers. This paper conducts an empirical investigation within a Rotterdam-based architecture office, with a broad portfolio in BIM usage and public building projects, to identify and categorize the factors affecting carbon data integration into the design workflow. It proposes a taxonomy of challenges within the architecture office, to better communicate the designer’s needs to the data providers and software developers with architects as a target user. Amongst the bottlenecks encountered are: access to data inclusiveness), data literacy and connecting data usage with design decision-making.

Nowadays, our society is in the transit to adopt more sustainable energy sources to reduce our impact on the environment; one alternative is solar energy. However, this is highly affected by the surroundings, which might cause shadowing effects. In this paper, we present our method to perform shadowing calculations in urban areas using semantic 3D city models, which is split into five sections: Point Grid Generation, Sun-Ray Generation, Nightside Filtering, Bounding Volume Hierarchy and the intersection between the sun rays and the BVH to identify which locations are shadowed at a given moment (epoch). Our tests are performed in Rotterdam’s city center, a dense urban area in The Netherlands. Our initial results indicate that the computational time per 100 k grid points fluctuates within 0.2–0.7s.

3D modeling of indoor spaces is a prerequisite for daylight simulation, and the accuracy of the 3D models has a significant impact on the simulation. The goal of this study was to quantify the errors caused by modeling indoor spaces at different accuracy levels to find the optimal balance between the reliability of the results and labor investment. For this purpose, we introduce a level of detail (LOD) concept for indoor spaces based on the size of non-permanent indoor objects by inclusion and exclusion from the simulation scene. The errors corresponding to models with low accuracies are measured by climate-based simulation using an improved two-phase method. Our results show that inaccurate modeling of indoor spaces causes between 10-70% error in TAI with 25% median across all spaces.

The potential of Building Information Models (BIM) to establish rights and responsibilities for multi-level building complexes in cadastral registrations has been explored in many previous researches. However, the implementation of BIM-based cadastral registrations in practice remains limited due to the complex interplay between technical potentials and legal implications as well as uncertainty about the additional complexity of a BIM model and the extra work that will be required to generate the BIM Legal model. In collaboration with Netherlands Kadaster, we have investigated the data requirements for a BIM Legal model that will support the 3D cadastral registration of apartment complexes and that aligns with BIM creation processes in practice. The BIM Legal model aims to visualise the spatial aspects of apartment complexes and to align the cadastral registration to the 3D data that is generated in the design and construction phase of new buildings. The BIM Legal file will be submitted when an apartment complex is being registered and is based on IFC, the open standard established by BuildingSMART International and used in the BIM domain. To make the implementation of BIM Legal feasible legally, technically, and economically, we apply a 3-phase approach. This paper presents the BIM Legal model as defined in Phase 1, which enables to generate a BIM Legal model compliant with current legislation frameworks with no/minimal manual interaction from BIM models as commonly generated in design processes. The paper describes the 3-phase approach, the context of Phase 1, the data requirement analyses, the defined BIM Legal data model, as well the questions that emerged during the specification process that need to be answered to further improve the implementation of Phase 1 and to further develop BIM Legal for Phases 2 and 3.

This paper introduces Property Valuation Application Domain Extension (ADE) within CityGML 3.0, aiming to integrate relevant indoor and outdoor 3D variables (cost estimation, view quality, etc.) for accurate property valuation. Current models lack the necessary features for this specific application. Leveraging IFC data for indoor elements, this ADE extends CityGML, addressing the existing gap. This paper identifies and categorizes data requirements, leading to the conceptualization and development of the model. By enriching CityGML 3.0 with IFC data, the approach introduces new features like the "Property Unit" to ensure adaptability across diverse valuation scenarios. Despite encountering data integrability challenges, we here commit to refining the model and overcoming these obstacles. A preliminary implementation using CityJSON demonstrates successful integration and paves the way for future implementation. These include developing an API platform and establishing an official repository to facilitate practical usability and scalability. This research significantly contributes to advancing property valuation processes by providing accurate valuations for stakeholders and promoting the use of 3D urban data in domain-specific extensions.

Urban building energy modelling aims at studying different approaches to estimate the energy consumed by buildings. However, most urban building energy models introduced in the literature ignore the impact that buildings have on their outdoor conditions, and inversely. Therefore, the paper determines whether interactions between buildings and outdoor conditions affects simulations performed using an urban building energy model, in particular during their calibration. Results suggest that interactions between buildings and their outdoor conditions affect the sensitivity of outcomes provided by an urban building energy model towards uncertain parameters and the probability to calibrate the model in accordance to some standards.

Accurate lane maps with semantics are crucial for various applications, such as high-definition maps (HD Maps), intelligent transportation systems (ITS), and digital twins. Manual annotation of lanes is labor-intensive and costly, prompting researchers to explore automatic lane extraction methods. This paper presents an end-to-end large-scale lane mapping method that considers both lane geometry and semantics. This study represents lane markings as polylines with uniformly sampled points and associated semantics, allowing for adaptation to varying lane shapes. Additionally, we propose an end-to-end network to extract lane polylines from mobile laser scanning (MLS) data, enabling the inference of vectorized lane instances without complex post-processing. The network consists of three components: a feature encoder, a column proposal generator, and a lane information decoder. The feature encoder encodes textual and structural information of lane markings to enhance the method’s robustness to data imperfections, such as varying lane intensity, uneven point density, and occlusion-induced incomplete data. The column proposal generator generates regions of interest for the subsequent decoder. Leveraging the embedded multi-scale features from the feature encoder, the lane decoder effectively predicts lane polylines and their associated semantics without requiring step-by-step conditional inference. Comprehensive experiments conducted on three lane datasets have demonstrated the performance of the proposed method, even in the presence of incomplete data and complex lane topology. Furthermore, the datasets used in this work, including source ground points, generated bird’s eye view (BEV) images, and annotations, will be publicly available with the publication of the paper.

The role and adoption of 3D city models have been changing from a data endpoint to a centralised data source that is used for a variety of different analyses in different sectors. This change has not yet been fully completed and the transition process is still very noticeable at certain places. For example, data required for city-scale analyses are often missing, incorrect, or not stored in a standard way. A subset of these data (E.g. shell volume, shell area & footprint area) can be approximated from lower LoD shapes (LoD2.2 or lower) in the 3D city models. However, these models frequently simplify reality and therefore these approximations are not accurate. This paper proposes computing these data by voxelising Building Information Modelling (BIM) models representing the same buildings as the 3D city model. It is shown that a subset of these approximations (shell volume & footprint area) are more accurate than values computed from lower LoD shapes. Storing these data as attributes of the building models in 3D city models can improve the ease of use and the outcome of city-scale analyses. The computed values from BIM models can also be assigned to outputs of BIM to Geo conversions. This overturns the accuracy loss of the geometry caused by the conversion in which geometry is significantly generalised and simplified.

In the evolving landscape of digitalization, automating building permit processes are crucial for municipalities and other governmental bodies. Our research addresses the complexities of modeling digital building permit regulations, considering the level of the information needs (LoIN) for geometry-based regulations in four municipalities (Prague, Lisbon, Vila Nova de Gaia, and Ascoli Piceno) as case study. We propose a methodology and guidelines for geometrical building modeling, addressing challenges of integrating geoinformation with Building Information Modeling (BIM) for environmental based digital building permit (DBP) checks. This paper provides BIM-IFC geometrical interpretation choices, ensuring a seamless conversion into 3D city models. It offers insights for software companies, developers, and standardization organizations towards in implementing DBP checks, and preliminary results for a future scalable approach.

The integration of 3D city models and Building Information Models (BIM) in the context of GeoBIM has gained significant attention from both academia and industry. Harmonizing the distinct characteristics and goals of these models is crucial for successful integration. In this paper, we present the development of a plugin for Autodesk Revit, a popular BIM platform, which allows for the incorporation of 3D Geo-data encoded in CityJSON. The plugin, published as open source, enables the generation of individual geometries with associated city model attributes as parameters, facilitating analysing the impact of new or changed buildings (modelled in BIM) on the environment (captured in geo-data). Challenges addressed during development include georeferencing, data format import, handling different geometry approaches, hierarchy of attributes, code optimization, user-friendliness, and enhanced visualization. The plugin contributes to the seamless integration of geo- and BIM data, enhancing interoperability and supporting informed decision-making in the Architecture, Engineering, and Construction and urban domains.